Nathaniel Forde
I'm a Data-Scientist working in HR Tech and People Analytics with Personio. I'm a big advocate of open source software and regularly contribute to PyMC, PyMC-Marketing and CausalPy. I've worked across a variety of industries ranging from e-commerce, insurance and gambling and in each, i've tried to find ways to apply statistical best practice to business problems.
I'm always open to chat about scientific python, philosophy of science and Bayesian reasoning and decision analysis.
Session
Dynamic Path Analysis (DPA) extends survival analysis with a causal, time-varying perspective. This allows causal effects to be decomposed into direct and indirect pathways that evolve over time. The perspective is particularly valuable when interventions (exercise) act through mediators (weight loss) whose influence changes dynamically in time, because we get to distil when each driver of our survival probabilities are active and whether their combined effects are harmful or positive.
Despite its conceptual appeal, DPA remains niche, with existing implementations limited to frequentist R packages and no Bayesian or Python-native alternatives. In this talk, I present a Bayesian, generative implementation of Dynamic Path Analysis using PyMC. By discretising time and modelling cumulative hazard effects with smooth spline priors, we obtain interpretable time-varying causal effects with coherent uncertainty quantification. I benchmark the approach against canonical dpasurv examples and discuss why DPA focuses on hazards rather than survival curves.
This talk is aimed at Python users interested in survival analysis, causal inference, and Bayesian modelling.